EFEKTIVITAS PENYALURAN BANTUAN LANGSUNG TUNAI DANA DESA BAGI KELUARGA PENERIMA MANFAAT (STUDI PADA DESA TURI KABUPATEN MAGETAN)
Bibliographic record
Abstract
Akibat Pandemi COVID-19 yang membawa dampak besar bagi perekonomian masyarakat, pemerintah berupaya mengatasinya dengan program Bantuan Langsung Tunai Dana Desa (BLT-DD). Dalam proses penyaluran BLT-DD masih terdapat permasalahan seperti kurangnya sosialisasi, permasalahan pendataan dan penetapan KPM, dan kriteria yang kurang sesuai. Penelitian ini bertujuan untuk mengetahui tingkat efektivitas penyaluran BLT-DD bagi Keluarga Penerima Manfaat di Desa Turi, Kabupaten Magetan. Penelitian ini merupakan penelitian deskriptif dengan pendekatan kuantitatif menggunakan 108 sampel yang dipilih menggunakan teknik probability sampling. Teknik pengumpulan data menggunakan kuesioner dan observasi. Teknik analisis data menggunakan statistik deskriptif dengan tujuh indikator efektivitas yaitu ketepatan waktu, ketepatan perhitungan biaya, ketepatan menentukan pilihan, ketepatan berpikir, ketepatan melakukan perintah, ketepatan menentukan tujuan, dan ketepatan sasaran. Hasil penelitian menunjukkan bahwa keseluruhan proses penyaluran BLT - Dana Desa di Desa Turi sudah berjalan sangat efektif dengan tingkat efektivitas 83,02%. Dari tujuh indikator, ada dua indikator yang masuk kategori Cukup Efektif yaitu indikator ketepatan melakukan perintah (69,35%) dan ketepatan sasaran (61,81%). Saran yang diberikan untuk dua indikator tersebut adalah pemerintah harusnya menetapkan standarisasi yang mudah diadaptasikan pada keadaan masyarakat saat ini serta melakukan pendataan secara selektif agar dalam penetapan KPM bisa tepat sasaran, melakukan sosialisasi kepada KPM terkait kriteria penerima, persyaratan administrasi, dan proses pengambilan bantuan agar informasi yang didapatkan oleh KPM bisa utuh dan menyeluruh. As a result of COVID-19 which had major impact on the community, the government has tried to overcome it with focusing Village Funds for Direct Cash Assistance (BLT-DD). In the distribution of BLT-DD there are still problems such as lack of socialization, problems with data collection and determination of beneficiaries (KPM), inappropriate criteria, etc. This research aims to determine the level of effectiveness of BLT-DD distribution for KPM in Turi Village, Magetan Regency. This research is a descriptive research with quantitative approach using 108 samples selected using probability sampling techniques. Data collection techniques used questionnaire and observation. Data analysis techniques used descriptive statistics with seven indicators of effectiveness that is timeliness, cost calculation accuracy, choice accuracy, thinking accuracy, command execution accuracy, goal determination accuracy, and target accuracy. The results of this research show that the entire process of distributing BLT-DD in Turi Village had been running very effectively with an effectiveness rate 83,02%. Of the seven indicators, there are two indicators that fall into category Moderately Effective, that is command execution accuracy (69,35%) and target accuracy (61,81%). The advice given for these indicators is that the government should set standards that are easy to adapt to the current state of society and selectively collect data so that determination of KPM can be right on target, conduct outreach to KPM regarding criteria for recipients, administrative requirements, and the process of taking aid so that the information obtained by KPM can be intact and comprehensive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".